Abstract
Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they require truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our experiments demonstrate the usefulness of our framework in both synthetic and real-world data.
Original language | English |
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Title of host publication | Proceedings of Machine Learning Research |
Subtitle of host publication | Asian Conference on Machine Learning, 20-22 November 2015, Hong Kong |
Editors | Geoffrey Holmes, Tie-Yan Liu |
Place of Publication | USA |
Publisher | Proceedings of Machine Learning Research (PMLR) |
Pages | 237-252 |
Number of pages | 16 |
Volume | 45 |
Publication status | Published - 1 Jan 2015 |
Externally published | Yes |
Event | Asian Conference on Machine Learning 2015 - Hong Kong, Hong Kong Duration: 20 Nov 2015 → 22 Nov 2015 Conference number: 7th http://acml-conf.org/2015/acml.php (Conference website) http://proceedings.mlr.press/v45/ (Proceedings) |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 45 |
ISSN (Print) | 1938-7228 |
Conference
Conference | Asian Conference on Machine Learning 2015 |
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Abbreviated title | ACML 2015 |
Country/Territory | Hong Kong |
City | Hong Kong |
Period | 20/11/15 → 22/11/15 |
Internet address |
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